Research Article
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Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India

Year 2024, , 31 - 45, 19.01.2024
https://doi.org/10.31127/tuje.1223779

Abstract

Machine Learning (ML) has been used in the prediction of geolocation with improved accuracies in this work. The pre-processed data was subjected to prediction analytics using 22 machine learning algorithms over regression mode. It was observed that Extra Trees Regressor performed well with better accuracies in predicting latitude, longitude, and Haversine distance, respectively. Regression models like CatBoost, Extreme Gradient boosting, Light Gradient boosting machine, and Gradient boosting regressor were also tested. The R2 values were computed for each case, and we obtained 0.96 (Longitude), 0.98 (Latitude), and 0.96 (Haversine), respectively. The evaluation of models was done using metrics like MAE, MASE, RMSE, R2, RMSLE, and MAPE and R2 is considered most important than others. The effect of data point was calculated using Cooks’ distance, and the variable fluoride has a significant impact on the prediction accuracy of Longitude followed by RSC, Cl, SO4, SAR, NO3, NA, Ca, EC and pH variables. In the prediction of latitude, the SAR variable played a significant role, followed by Na and TH. According to the t-SNE manifold, three longitude values were quite different from the others. This work is supported by some of the manifests like Cooks’ distance outlier detection, feature importance plot, t-SNE manifold, prediction error plot, residuals plot, RFECV plot, and validation curve. This work is done to report that the challenge of predicting both latitude and longitude on a common ground is solved partially, if not completely, and machine learning tools can be used for this purpose. Haversine distance can be obtained from latitude and longitude and can be used in the prediction of geolocation.

Supporting Institution

None

Project Number

None

Thanks

Central Groundwater Control Board, Government of India

References

  • Afzaal, H., Farooque, A. A., Abbas, F., Acharya, B., & Esau, T. (2019). Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning. Water, 12(1), 5. https://doi.org/10.3390/w12010005
  • Azdy, R. A., & Darnis, F. (2020, April). Use of haversine formula in finding distance between temporary shelter and waste end processing sites. In Journal of Physics: Conference Series, 1500(1), 012104. https://doi.org/10.1088/1742-6596/1500/1/012104
  • Ahmed, U., Mumtaz, R., Anwar, H., Shah, A. A., Irfan, R., & García-Nieto, J. (2019). Efficient water quality prediction using supervised machine learning. Water, 11(11), 2210. https://doi.org/10.3390/w11112210
  • Alizamir, M., Kisi, O., & Zounemat-Kermani, M. (2018). Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrological sciences journal, 63(1), 63-73. https://doi.org/10.1080/02626667.2017.1410891
  • Alkan, H., & Celebi, H. (2019). The Implementation of Positioning System with Trilateration of Haversine Distance. 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 1–6. Istanbul, Turkey: IEEE. https://doi.org/10.1109/PIMRC.2019.8904289
  • Bowes, B. D., Sadler, J. M., Morsy, M. M., Behl, M., & Goodall, J. L. (2019). Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks. Water, 11(5), 1098. https://doi.org/10.3390/w11051098
  • Dwivedi, P., Khan, A. A., Mudge, S., & Sharma, G. (2022). Explainable AI (XAI) for Social Good: Leveraging AutoML to Assess and Analyze Vital Potable Water Quality Indicators. In Computational Intelligence in Data Mining: Proceedings of ICCIDM 2021 (pp. 591-606). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-9447-9_45
  • Gupta, P. K., Yadav, B., & Yadav, B. K. (2019). Assessment of LNAPL in subsurface under fluctuating groundwater table using 2D sand tank experiments. Journal of Environmental Engineering, 145(9), 04019048. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001560
  • Izady, A., Davary, K., Alizadeh, A., Ziaei, A. N., Alipoor, A., Joodavi, A., & Brusseau, M. L. (2014). A framework toward developing a groundwater conceptual model. Arabian Journal of Geosciences, 7, 3611-3631. https://doi.org/10.1007/s12517-013-0971-9
  • Jamin, P., Cochand, M., Dagenais, S., Lemieux, J. M., Fortier, R., Molson, J., & Brouyère, S. (2020). Direct measurement of groundwater flux in aquifers within the discontinuous permafrost zone: an application of the finite volume point dilution method near Umiujaq (Nunavik, Canada). Hydrogeology Journal, 28(3), 869-885. https://doi.org/10.1007/s10040-020-02108-y
  • Kim, G. B. (2020). A study on the establishment of groundwater protection area around a saline waterway by combining artificial neural network and GIS-based AHP. Environmental Earth Sciences, 79(5), 117. https://doi.org/10.1007/s12665-020-8862-3
  • Kuhn, M., & Johnson, K. (2013). Applied predictive modeling, 26, 13. New York: Springer.
  • Larsen, E., Noever, D., MacVittie, K., & Lilly, J. (2021). Overhead-MNIST: Machine Learning Baselines for Image Classification. https://doi.org/10.48550/ARXIV.2107.00436
  • Mallikarjuna, B., Sathish, K., Venkata Krishna, P., & Viswanathan, R. (2021). The effective SVM-based binary prediction of ground water table. Evolutionary Intelligence, 14(2), 779–787. https://doi.org/10.1007/s12065-020-00447-z
  • Meyer, H., Reudenbach, C., Wöllauer, S., & Nauss, T. (2019). Importance of spatial predictor variable selection in machine learning applications–Moving from data reproduction to spatial prediction. Ecological Modelling, 411, 108815. https://doi.org/10.1016/j.ecolmodel.2019.108815
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  • Omar, P. J., Gaur, S., Dwivedi, S. B., & Dikshit, P. K. S. (2019). Groundwater modelling using an analytic element method and finite difference method: an insight into Lower Ganga River basin. Journal of Earth System Science, 128, 195. https://doi.org/10.1007/s12040-019-1225-3
  • Pant, R. R., Zhang, F., Rehman, F. U., Wang, G., Ye, M., Zeng, C., & Tang, H. (2018). Spatiotemporal variations of hydrogeochemistry and its controlling factors in the Gandaki River Basin, Central Himalaya Nepal. Science of the Total Environment, 622, 770-782. https://doi.org/10.1016/j.scitotenv.2017.12.063
  • Pham, B. T., Jaafari, A., Prakash, I., Singh, S. K., Quoc, N. K., & Bui, D. T. (2019). Hybrid computational intelligence models for groundwater potential mapping. Catena, 182, 104101. https://doi.org/10.1016/j.catena.2019.104101
  • Xin, L., & Mou, T. (2022). Research on the Application of Multimodal-Based Machine Learning Algorithms to Water Quality Classification. Wireless Communications and Mobile Computing, 2022, 9555790. https://doi.org/10.1155/2022/9555790
  • Xue, J., Huo, Z., Wang, F., Kang, S., & Huang, G. (2018). Untangling the effects of shallow groundwater and deficit irrigation on irrigation water productivity in arid region: New conceptual model. Science of The Total Environment, 619, 1170-1182. https://doi.org/10.1016/j.scitotenv.2017.11.145
  • Çubukçu, E. A., Demir, V., & Sevimli, M. F. (2022). Digital elevation modeling using artificial neural networks, deterministic and geostatistical interpolation methods. Turkish Journal of Engineering, 6(3), 199-205. https://doi.org/10.31127/tuje.889570
  • Demir, V., & Citakoglu, H. (2023). Forecasting of solar radiation using different machine learning approaches. Neural Computing and Applications, 35(1), 887-906. https://doi.org/10.1007/s00521-022-07841-x
  • Demir, V., & Yaseen, Z. M. (2023). Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review. Neural Computing and Applications, 35(1), 303-343. https://doi.org/10.1007/s00521-022-07699-z
  • Citakoglu, H., & Demir, V. (2023). Developing numerical equality to regional intensity–duration–frequency curves using evolutionary algorithms and multi-gene genetic programming. Acta Geophysica, 71(1), 469-488. https://doi.org/10.1007/s00521-022-07699-z
  • https://indiawris.gov.in/wris/
  • http://cgwb.gov.in/GW-data-access.html
  • https://scikit-learn.org/stable/supervised_learning.html#supervised-learning
  • https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html
Year 2024, , 31 - 45, 19.01.2024
https://doi.org/10.31127/tuje.1223779

Abstract

Project Number

None

References

  • Afzaal, H., Farooque, A. A., Abbas, F., Acharya, B., & Esau, T. (2019). Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning. Water, 12(1), 5. https://doi.org/10.3390/w12010005
  • Azdy, R. A., & Darnis, F. (2020, April). Use of haversine formula in finding distance between temporary shelter and waste end processing sites. In Journal of Physics: Conference Series, 1500(1), 012104. https://doi.org/10.1088/1742-6596/1500/1/012104
  • Ahmed, U., Mumtaz, R., Anwar, H., Shah, A. A., Irfan, R., & García-Nieto, J. (2019). Efficient water quality prediction using supervised machine learning. Water, 11(11), 2210. https://doi.org/10.3390/w11112210
  • Alizamir, M., Kisi, O., & Zounemat-Kermani, M. (2018). Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data. Hydrological sciences journal, 63(1), 63-73. https://doi.org/10.1080/02626667.2017.1410891
  • Alkan, H., & Celebi, H. (2019). The Implementation of Positioning System with Trilateration of Haversine Distance. 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 1–6. Istanbul, Turkey: IEEE. https://doi.org/10.1109/PIMRC.2019.8904289
  • Bowes, B. D., Sadler, J. M., Morsy, M. M., Behl, M., & Goodall, J. L. (2019). Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks. Water, 11(5), 1098. https://doi.org/10.3390/w11051098
  • Dwivedi, P., Khan, A. A., Mudge, S., & Sharma, G. (2022). Explainable AI (XAI) for Social Good: Leveraging AutoML to Assess and Analyze Vital Potable Water Quality Indicators. In Computational Intelligence in Data Mining: Proceedings of ICCIDM 2021 (pp. 591-606). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-9447-9_45
  • Gupta, P. K., Yadav, B., & Yadav, B. K. (2019). Assessment of LNAPL in subsurface under fluctuating groundwater table using 2D sand tank experiments. Journal of Environmental Engineering, 145(9), 04019048. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001560
  • Izady, A., Davary, K., Alizadeh, A., Ziaei, A. N., Alipoor, A., Joodavi, A., & Brusseau, M. L. (2014). A framework toward developing a groundwater conceptual model. Arabian Journal of Geosciences, 7, 3611-3631. https://doi.org/10.1007/s12517-013-0971-9
  • Jamin, P., Cochand, M., Dagenais, S., Lemieux, J. M., Fortier, R., Molson, J., & Brouyère, S. (2020). Direct measurement of groundwater flux in aquifers within the discontinuous permafrost zone: an application of the finite volume point dilution method near Umiujaq (Nunavik, Canada). Hydrogeology Journal, 28(3), 869-885. https://doi.org/10.1007/s10040-020-02108-y
  • Kim, G. B. (2020). A study on the establishment of groundwater protection area around a saline waterway by combining artificial neural network and GIS-based AHP. Environmental Earth Sciences, 79(5), 117. https://doi.org/10.1007/s12665-020-8862-3
  • Kuhn, M., & Johnson, K. (2013). Applied predictive modeling, 26, 13. New York: Springer.
  • Larsen, E., Noever, D., MacVittie, K., & Lilly, J. (2021). Overhead-MNIST: Machine Learning Baselines for Image Classification. https://doi.org/10.48550/ARXIV.2107.00436
  • Mallikarjuna, B., Sathish, K., Venkata Krishna, P., & Viswanathan, R. (2021). The effective SVM-based binary prediction of ground water table. Evolutionary Intelligence, 14(2), 779–787. https://doi.org/10.1007/s12065-020-00447-z
  • Meyer, H., Reudenbach, C., Wöllauer, S., & Nauss, T. (2019). Importance of spatial predictor variable selection in machine learning applications–Moving from data reproduction to spatial prediction. Ecological Modelling, 411, 108815. https://doi.org/10.1016/j.ecolmodel.2019.108815
  • Moez, A. (2020). PyCaret: An open source, low-code machine learning library in Python. PyCaret, Apr.
  • Mukherjee, A., Duttagupta, S., Chattopadhyay, S., Bhanja, S. N., Bhattacharya, A., Chakraborty, S., ... & Sahu, S. (2019). Impact of sanitation and socio-economy on groundwater fecal pollution and human health towards achieving sustainable development goals across India from ground-observations and satellite-derived nightlight. Scientific Reports, 9(1), 15193.
  • Omar, P. J., Gaur, S., Dwivedi, S. B., & Dikshit, P. K. S. (2019). Groundwater modelling using an analytic element method and finite difference method: an insight into Lower Ganga River basin. Journal of Earth System Science, 128, 195. https://doi.org/10.1007/s12040-019-1225-3
  • Pant, R. R., Zhang, F., Rehman, F. U., Wang, G., Ye, M., Zeng, C., & Tang, H. (2018). Spatiotemporal variations of hydrogeochemistry and its controlling factors in the Gandaki River Basin, Central Himalaya Nepal. Science of the Total Environment, 622, 770-782. https://doi.org/10.1016/j.scitotenv.2017.12.063
  • Pham, B. T., Jaafari, A., Prakash, I., Singh, S. K., Quoc, N. K., & Bui, D. T. (2019). Hybrid computational intelligence models for groundwater potential mapping. Catena, 182, 104101. https://doi.org/10.1016/j.catena.2019.104101
  • Xin, L., & Mou, T. (2022). Research on the Application of Multimodal-Based Machine Learning Algorithms to Water Quality Classification. Wireless Communications and Mobile Computing, 2022, 9555790. https://doi.org/10.1155/2022/9555790
  • Xue, J., Huo, Z., Wang, F., Kang, S., & Huang, G. (2018). Untangling the effects of shallow groundwater and deficit irrigation on irrigation water productivity in arid region: New conceptual model. Science of The Total Environment, 619, 1170-1182. https://doi.org/10.1016/j.scitotenv.2017.11.145
  • Çubukçu, E. A., Demir, V., & Sevimli, M. F. (2022). Digital elevation modeling using artificial neural networks, deterministic and geostatistical interpolation methods. Turkish Journal of Engineering, 6(3), 199-205. https://doi.org/10.31127/tuje.889570
  • Demir, V., & Citakoglu, H. (2023). Forecasting of solar radiation using different machine learning approaches. Neural Computing and Applications, 35(1), 887-906. https://doi.org/10.1007/s00521-022-07841-x
  • Demir, V., & Yaseen, Z. M. (2023). Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review. Neural Computing and Applications, 35(1), 303-343. https://doi.org/10.1007/s00521-022-07699-z
  • Citakoglu, H., & Demir, V. (2023). Developing numerical equality to regional intensity–duration–frequency curves using evolutionary algorithms and multi-gene genetic programming. Acta Geophysica, 71(1), 469-488. https://doi.org/10.1007/s00521-022-07699-z
  • https://indiawris.gov.in/wris/
  • http://cgwb.gov.in/GW-data-access.html
  • https://scikit-learn.org/stable/supervised_learning.html#supervised-learning
  • https://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html
There are 30 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Jagadish Kumar Mogaraju 0000-0002-6461-8614

Project Number None
Early Pub Date September 15, 2023
Publication Date January 19, 2024
Published in Issue Year 2024

Cite

APA Mogaraju, J. K. (2024). Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. Turkish Journal of Engineering, 8(1), 31-45. https://doi.org/10.31127/tuje.1223779
AMA Mogaraju JK. Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. TUJE. January 2024;8(1):31-45. doi:10.31127/tuje.1223779
Chicago Mogaraju, Jagadish Kumar. “Machine Learning Empowered Prediction of Geolocation Using Groundwater Quality Variables over YSR District of India”. Turkish Journal of Engineering 8, no. 1 (January 2024): 31-45. https://doi.org/10.31127/tuje.1223779.
EndNote Mogaraju JK (January 1, 2024) Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. Turkish Journal of Engineering 8 1 31–45.
IEEE J. K. Mogaraju, “Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India”, TUJE, vol. 8, no. 1, pp. 31–45, 2024, doi: 10.31127/tuje.1223779.
ISNAD Mogaraju, Jagadish Kumar. “Machine Learning Empowered Prediction of Geolocation Using Groundwater Quality Variables over YSR District of India”. Turkish Journal of Engineering 8/1 (January 2024), 31-45. https://doi.org/10.31127/tuje.1223779.
JAMA Mogaraju JK. Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. TUJE. 2024;8:31–45.
MLA Mogaraju, Jagadish Kumar. “Machine Learning Empowered Prediction of Geolocation Using Groundwater Quality Variables over YSR District of India”. Turkish Journal of Engineering, vol. 8, no. 1, 2024, pp. 31-45, doi:10.31127/tuje.1223779.
Vancouver Mogaraju JK. Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. TUJE. 2024;8(1):31-45.
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